[
https://issues.apache.org/jira/browse/SPARK-35066?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Erik Krogen updated SPARK-35066:
--------------------------------
Description:
Hi,
The following snippet code runs 4-5 times slower when it's used in Apache Spark
or PySpark 3.1.1 compare to Apache Spark or PySpark 3.0.2:
{code:java}
spark = SparkSession.builder \
.master("local[*]") \
.config("spark.driver.memory", "16G") \
.config("spark.driver.maxResultSize", "0") \
.config("spark.serializer",
"org.apache.spark.serializer.KryoSerializer") \
.config("spark.kryoserializer.buffer.max", "2000m") \
.getOrCreate()
Toys = spark.read \
.parquet('./toys-cleaned').repartition(12)
# tokenize the text
regexTokenizer = RegexTokenizer(inputCol="reviewText",
outputCol="all_words", pattern="\\W")
toys_with_words = regexTokenizer.transform(Toys)
# remove stop words
remover = StopWordsRemover(inputCol="all_words", outputCol="words")
toys_with_tokens = remover.transform(toys_with_words).drop("all_words")
all_words = toys_with_tokens.select(explode("words").alias("word"))
# group by, sort and limit to 50k
top50k =
all_words.groupBy("word").agg(count("*").alias("total")).sort(col("total").desc()).limit(50000)
top50k.show()
{code}
Some debugging on my side revealed that in Spark/PySpark 3.0.2 the 12
partitions are respected in a way that all 12 tasks are being processed
altogether. However, in Spark/PySpark 3.1.1 even though we have 12 tasks, 10 of
them finish immediately and only 2 are being processed. (I've tried to disable
a couple of configs related to something similar, but none of them worked)
Screenshot of spark 3.1.1 task:
!image-2022-03-17-17-18-36-793.png|width=1073,height=652!
!image-2022-03-17-17-19-11-655.png!
Screenshot of spark 3.0.2 task:
!image-2022-03-17-17-19-34-906.png!
For a longer discussion: [Spark User List
|https://lists.apache.org/thread/1hlg9fpxnw8dzx8bd2fvffmk7yozoszf]
You can reproduce this big difference of performance between Spark 3.1.1 and
Spark 3.0.2 by using the shared code with any dataset that is large enough to
take longer than a minute. Not sure if this is related to SQL, any Spark config
being enabled in 3.x but not really into action before 3.1.1, or it's about
.transform in Spark ML.
was:
Hi,
The following snippet code runs 4-5 times slower when it's used in Apache Spark
or PySpark 3.1.1 compare to Apache Spark or PySpark 3.0.2:
{code:java}
spark = SparkSession.builder \
.master("local[*]") \
.config("spark.driver.memory", "16G") \
.config("spark.driver.maxResultSize", "0") \
.config("spark.serializer",
"org.apache.spark.serializer.KryoSerializer") \
.config("spark.kryoserializer.buffer.max", "2000m") \
.getOrCreate()
Toys = spark.read \
.parquet('./toys-cleaned').repartition(12)
# tokenize the text
regexTokenizer = RegexTokenizer(inputCol="reviewText",
outputCol="all_words", pattern="\\W")
toys_with_words = regexTokenizer.transform(Toys)
# remove stop words
remover = StopWordsRemover(inputCol="all_words", outputCol="words")
toys_with_tokens = remover.transform(toys_with_words).drop("all_words")
all_words = toys_with_tokens.select(explode("words").alias("word"))
# group by, sort and limit to 50k
top50k =
all_words.groupBy("word").agg(count("*").alias("total")).sort(col("total").desc()).limit(50000)
top50k.show()
{code}
Some debugging on my side revealed that in Spark/PySpark 3.0.2 the 12
partitions are respected in a way that all 12 tasks are being processed
altogether. However, in Spark/PySpark 3.1.1 even though we have 12 tasks, 10 of
them finish immediately and only 2 are being processed. (I've tried to disable
a couple of configs related to something similar, but none of them worked)
Screenshot of spark 3.1.1 task:
!image-2022-03-17-17-18-36-793.png|width=1073,height=652!
!image-2022-03-17-17-19-11-655.png!
Screenshot of spark 3.0.2 task:
!image-2022-03-17-17-19-34-906.png!
For a longer discussion: [Spark User List
|https://lists.apache.org/thread/1bslwjdwnr5tw7wjkv0672vj41x4g2f1]
You can reproduce this big difference of performance between Spark 3.1.1 and
Spark 3.0.2 by using the shared code with any dataset that is large enough to
take longer than a minute. Not sure if this is related to SQL, any Spark config
being enabled in 3.x but not really into action before 3.1.1, or it's about
.transform in Spark ML.
> Spark 3.1.1 is slower than 3.0.2 by 4-5 times
> ---------------------------------------------
>
> Key: SPARK-35066
> URL: https://issues.apache.org/jira/browse/SPARK-35066
> Project: Spark
> Issue Type: Bug
> Components: ML, SQL
> Affects Versions: 3.1.1
> Environment: Spark/PySpark: 3.1.1
> Language: Python 3.7.x / Scala 12
> OS: macOS, Linux, and Windows
> Cloud: Databricks 7.3 for 3.0.1 and 8 for 3.1.1
> Reporter: Maziyar PANAHI
> Priority: Major
> Attachments: Screenshot 2021-04-07 at 11.15.48.png, Screenshot
> 2021-04-08 at 15.08.09.png, Screenshot 2021-04-08 at 15.13.19-1.png,
> Screenshot 2021-04-08 at 15.13.19.png, image-2022-03-17-17-18-36-793.png,
> image-2022-03-17-17-19-11-655.png, image-2022-03-17-17-19-34-906.png
>
>
> Hi,
> The following snippet code runs 4-5 times slower when it's used in Apache
> Spark or PySpark 3.1.1 compare to Apache Spark or PySpark 3.0.2:
>
> {code:java}
> spark = SparkSession.builder \
> .master("local[*]") \
> .config("spark.driver.memory", "16G") \
> .config("spark.driver.maxResultSize", "0") \
> .config("spark.serializer",
> "org.apache.spark.serializer.KryoSerializer") \
> .config("spark.kryoserializer.buffer.max", "2000m") \
> .getOrCreate()
> Toys = spark.read \
> .parquet('./toys-cleaned').repartition(12)
> # tokenize the text
> regexTokenizer = RegexTokenizer(inputCol="reviewText",
> outputCol="all_words", pattern="\\W")
> toys_with_words = regexTokenizer.transform(Toys)
> # remove stop words
> remover = StopWordsRemover(inputCol="all_words", outputCol="words")
> toys_with_tokens = remover.transform(toys_with_words).drop("all_words")
> all_words = toys_with_tokens.select(explode("words").alias("word"))
> # group by, sort and limit to 50k
> top50k =
> all_words.groupBy("word").agg(count("*").alias("total")).sort(col("total").desc()).limit(50000)
> top50k.show()
> {code}
>
> Some debugging on my side revealed that in Spark/PySpark 3.0.2 the 12
> partitions are respected in a way that all 12 tasks are being processed
> altogether. However, in Spark/PySpark 3.1.1 even though we have 12 tasks, 10
> of them finish immediately and only 2 are being processed. (I've tried to
> disable a couple of configs related to something similar, but none of them
> worked)
> Screenshot of spark 3.1.1 task:
> !image-2022-03-17-17-18-36-793.png|width=1073,height=652!
> !image-2022-03-17-17-19-11-655.png!
>
> Screenshot of spark 3.0.2 task:
>
> !image-2022-03-17-17-19-34-906.png!
> For a longer discussion: [Spark User List
> |https://lists.apache.org/thread/1hlg9fpxnw8dzx8bd2fvffmk7yozoszf]
>
> You can reproduce this big difference of performance between Spark 3.1.1 and
> Spark 3.0.2 by using the shared code with any dataset that is large enough to
> take longer than a minute. Not sure if this is related to SQL, any Spark
> config being enabled in 3.x but not really into action before 3.1.1, or it's
> about .transform in Spark ML.
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